基于模拟退火机制的多种群萤火虫算法  

Multi-group firefly algorithm based on simulated annealing mechanism

作　　者：王铭波 符强[1,2] 童楠[1] 刘政[1] 赵一鸣[1] 

机构地区：[1]宁波大学科学技术学院,浙江宁波315212 [2]宁波大学信息科学与工程学院,浙江宁波315211

出　　处：《计算机应用》2015年第3期691-695,共5页journal of Computer Applications

基　　金：浙江省教育厅科研项目(Y201326770);浙江省大学生新苗人才计划项目(2014R405066);十二五浙江省重点学科建设项目(科[2012]80-314);宁波市自然科学基金资助项目(2014A610069)

摘　　要：针对传统萤火虫算法(FA)中存在的过早收敛和易陷入局部最优解等问题,提出了一种基于模拟退火机制的多种群萤火虫算法(MFA_SA):将萤火虫种群平均分为参数不同的多个子种群。为了防止算法陷入局部最优解,利用模拟退火机制大概率接受较好的解,小概率接受较差的解。同时,在种群寻优的过程中引入可变的距离权重,通过萤火虫算法的迭代次数动态调整萤火虫的"视野"范围。利用5个标准测试函数对该算法进行了对比仿真测试,结果表明,该算法在4个测试函数中均能寻找到全局最优解,并且在最优值、平均值、方差等指标上均比对比算法高出多个数量级,验证了新算法的有效性。According to the problem of premature convergence and local optimum in Firefly Algorithm （FA）, this paper came up with a kind of multi-group firefly algorithm based on simulated annealing mechanism （MFA_SA）, which equally divided firefly populations into many child populations with different parameter. To prevent algorithm fall into local optimum, simulated annealing mechanism was adopted to accept good solutions by the big probability, and keep bad solutions by the small probability. Meanwhile, variable distance weight was led into the process of population optimization to dynamically adjust the “vision” of firefly individual. Experiments were conducted on 5 kinds of benchmark functions between MFA_SA and three comparison algorithms. The experimental results show that, MFA_SA can find the global optimal solutions in 4 testing function, and achieve much better optimal solution, average and variance than other comparison algorithms, which demonstrates the effectiveness of the new algorithm.

关 键 词：萤火虫算法 模拟退火机制 多种群 距离权重 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]